Spatial data mining to support pandemic preparedness
ACM SIGKDD Explorations Newsletter
Guest Editors' Introduction: Urban Computing
IEEE Pervasive Computing
Mining correlation between locations using human location history
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Unifying dependent clustering and disparate clustering for non-homogeneous data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Driving with knowledge from the physical world
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering spatio-temporal causal interactions in traffic data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving Energy Use Forecast for Campus Micro-grids Using Indirect Indicators
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence
Communications of the ACM
Discovering regions of different functions in a city using human mobility and POIs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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The confluence of several developments has created an opportune moment for energy system modernization. In the past decade, smart grids have attracted many research activities in different domains. To realize the next generation of smart grids, we must have a comprehensive understanding of interdependent networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this paper, we develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. We demonstrate the multiple factors that can be simultaneously leveraged in our framework in order to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.